This invention relates to telematics, and more particularly to the use of personal mobile devices for such telematics.
Telematics includes the use of a combination of one or more of computing, communication, positioning systems, and sensor technologies for vehicle monitoring and navigation tasks. An important requirement in many telematics applications is to determine the trajectory (path) taken by a vehicle on a road network, a task referred to as “map matching”. To aid map matching, a digitized model of a road network is represented as interconnected road segments, and some positioning technology located in the vehicle is used to obtain approximate positions as a function of time. In most current approaches, positioning information is obtained using a Global Positioning System (GPS) receiver in the vehicle. This location information has inherent uncertainty, and may also suffer from outages in certain areas of the world, such as around tall buildings, in “urban canyons”, or inside tunnels. However, when combined with the model of the road network, and in some cases with a model of a vehicle's motion on the road network, an estimate of the trajectory of the vehicle on the road network can be computed. Another aspect of telematics is determining the velocity and acceleration of a vehicle as a function of time. A common approach is to use GPS for this purpose, using the GPS receiver's velocity estimates (computed after some smoothing) and computing its time-derivative to estimate acceleration, or even computing time-derivatives of GPS positions to estimate the velocity and acceleration of the vehicle. Another approach is to use embedded sensor-equipped hardware such as a MEMS accelerometer mounted in a known position and orientation to obtain this information (either using sensors on the hardware or from the vehicle's internal sensors).
To cope with the uncertainty in position estimates, one approach to map matching is to use a Hidden Markov Model (HMM). HMM approaches make use of a prior model in which a vehicle's path through a road network is assumed to obey a Markov (memoryless) process, and the position observations made at successive times are assumed to conditionally independent of the road segment the vehicle was on at that time point. Each position observation yields a probability distribution of a vehicle's location on the road network given only that measurement, and the task is to determine the hidden states (in this case, the road segments along the path) of the Markov process that best explain the uncertain position observations. HMM techniques, such as the Viterbi Algorithm, provide a way to perform inference on this Markovian probability model to determine the most likely path by taking into account the observations at previous time-steps, the inferred vehicle partial paths and prior beliefs on these vehicle paths. By computing scores for various paths between observations, an HMM produces a trajectory. This method has been applied to GPS data in Krumm et al., “Map Matching with Travel Time Constraints,” SAE World Congress, 2007, and to GPS as well 802.11-radio-based position data by Thiagarajan et al., “VTrack: Accurate, Energy-aware Road Traffic Delay Estimation using Mobile Phones”, SenSys 2009. These are incorporated herein by reference.
It is well known that GPS location may be difficult to acquire or is inaccurate in some locations, for example, in so-called urban canyons in locations near tall buildings, as well as inside tunnels. As an alternative, approaches have been developed that use mapped locations of wireless local area network (802.11, also known as “WiFi”) transmitters (e.g., access points) to provide estimates of a location of a receiver. However, such WiFi-based location estimates may have high error as well, typically between 50 meters and 500 meters. In addition, WiFi access point transmitters could have moved or gone offline since the original mapping was done, increasing and error and uncertainty in the position estimate. An alternative to WiFi position data is cellular position data, which is based on mapped locations of cellular base stations. Such position estimates can have errors between several hundred meters and up to 4 or 5 kilometers from the true position because of the longer range of cellular radios.
An approach to use HMM techniques to process WiFi position data are described in Thiagarajan et al., “VTrack: Accurate, Energy-aware Road Traffic Delay Estimation using Mobile Phones”, SenSys 2009. An approach to use HMM techniques with cellular position data is described in Thiagarajan et al., “Accurate, Low-Energy Trajectory Mapping for Mobile Devices”, 8th USENIX Symp. on Networked Systems Design and Implementation (NSDI), 2011. These are incorporated herein by reference.
On personal communication devices such as smartphones and mobile phones, telematics implemented with frequent GPS sampling results in unacceptably short battery life, because the GPS chips performing the sampling often consume significant power. Overcoming this shortcoming to enable energy-efficient telematics on personal mobile devices requires new methods.